In the development of digital agriculture, agricultural robots play a unique role and confer numerous advantages in farming production. From the invention of the first industrial robots in the 1950s, robots have begun to capture the attention of both research and industry. Thanks to the recent advancements in computer science, sensing, and control approaches, agricultural robots have experienced a rapid evolution, relying on various cutting-edge technologies for different application scenarios. Indeed, significant refinements have been achieved by integrating perception, decision-making, control, and execution techniques. However, most agricultural robots continue to require intelligence solutions, limiting them to small-scale applications without quantity production because of their lack of integration with artificial intelligence. Therefore, to help researchers and engineers grasp the prevalent research status of agricultural robots, in this review we refer to more than 100 pieces of literature according to the category of agricultural robots under discussion. In this context, we bring together diverse agricultural robot research statuses and applications and discuss the benefits and challenges involved in further applications. Finally, directional indications are put forward with respect to the research trends relating to agricultural robots.

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Recent Advancements in Agriculture Robots: Benefits and Challenges

Asphalt pavements absorb and store more heat than natural surfaces. Thus, high temperatures are emitted from conventional asphalt pavements, subsequently releasing heat into the atmosphere and contributing to the urban heat island (UHI) phenomenon. Several cool pavement strategies, including the provision of additives and materials, surface coating and layer design, have been introduced to reduce the impact of UHI. This article provides a detailed review of the thermal properties of these mitigation measures in the context of cool asphalt pavements. The literature can be divided into three segments. The first segment discusses the impact of pavements on UHI and heat transfer mechanisms in pavements. The second segment focuses on various thermo physical properties that play an important role in mitigation measures; these properties include albedo (α), emissivity (e), solar reflective index, thermal conductivity (k), specific heat capacity (Cp) and thermal diffusivity. The third segment discusses cool asphalt pavement strategies which specifically cover the ability of the pavement to absorb and reflect solar energy on the basis of the materials and treatments used. The literature reveals that cooling strategies that deal with the pavement surface are important due to its direct incident solar effect, which depends on surface colour, material, shape and roughness. By using high-albedo and high-emissivity surfaces, the pavement can store less heat and lower the surface temperature. These results can also be achieved by designing the materials and pavement layers with low thermal conductivity and high specific heat capacity to reduce thermal diffusivity and pavement temperature and thus combat the heat radiated by the asphalt pavement.

Thermal performance of cooling strategies for asphalt pavement: A state-of-the-art review

A review on the heat transfer in asphalt pavements and urban heat island mitigation methods

This study conducts an experimental programme to investigate the possibility of utilising a binary combination of red mud (RM) and slag in the form of alkali-activated slag-RM (AASR) as a useful ma...

Thermo- mechanical properties of alkali-activated slag–Red mud concrete

Effect of Aggregate Gradation and Asphalt Mix Volumetrics on the Thermal Properties of Asphalt Concrete

Corn harvester row alignment is a critical factor to improve harvesting quality and reduce cob drop loss. In this paper, a corn harvester automatic row alignment system is designed with a self-propelled corn combine harvester, incorporating a front-end touch row alignment mechanism of the cutting table and a harvester steering control system. Corn stalk lateral deviation from the reference row during harvesting is detected by a front-end touch-row alignment mechanism that serves as an input to the automatic alignment system. The harvester steering control system consists of Hirschmann PLC controller, electric steering wheel, steering wheel deflection angle detection device, display module and mode selection module, etc. The adaptive fuzzy PID control algorithm is used to determine the desired turning angle of the harvester steering wheel by combining with the harvester kinematic model, and the model is simulated and analyzed by Matlab/Simulink software. The automatic row alignment system was mounted on a 4LZ-8 self-propelled corn harvester for field tests, and the test results showed that the average percentage of deviation of corn stalks from the center of the row alignment cutting path within ±15 cm during the automatic row alignment process was 95.4% at harvester speeds ranging from 0 to 4.6 km/h, which could meet the requirements of the corn harvester for row alignment harvesting. The test results meet the requirements of the corn harvester for row alignment and serve as a benchmark for the research on automatic row alignment of corn harvesters.

/pdf/development-and-testing-of-automatic-row-alignment-system-3c4dk5hn.pdf

Development and Testing of Automatic Row Alignment System for Corn Harvesters

Measuring the Thermal Properties of Pavement Materials

In order to realize the intelligent identification of maize leaf diseases for accurate prevention and control, this study proposed a maize disease detection method based on improved MobileNet V3-small, using a UAV to collect maize disease images and establish a maize disease dataset in a complex context, and explored the effects of data expansion and migration learning on model recognition accuracy, recall rate, and F1-score instructive evaluative indexes, and the results show that the two approaches of data expansion and migration learning effectively improved the accuracy of the model. The structured compression of MobileNet V3-small bneck layer retains only 6 layers, the expansion multiplier of each layer was redesigned, 32-fold fast downsampling was used in the first layer, and the location of the SE module was optimized. The improved model had an average accuracy of 79.52% in the test set, a recall of 77.91%, an F1-score of 78.62%, a model size of 2.36 MB, and a single image detection speed of 9.02 ms. The detection accuracy and speed of the model can meet the requirements of mobile or embedded devices. This study provides technical support for realizing the intelligent detection of maize leaf diseases. Keywords: maize leaf disease, image recognition, model compression, MobileNetV3-small DOI: 10.25165/j.ijabe.20231603.7799 Citation: Gao A, Geng A J, Song Y P, Ren L L, Zhang Y, Han X. Detection of maize leaf diseases using improved MobileNet V3-small. Int J Agric & Biol Eng, 2023; 16(3): 225–232. 

Detection of maize leaf diseases using improved MobileNet V3-small

: Traditional maize ear harvesters mainly rely on manual identification of fallen maize ears, which cannot realize real-time detection of ear falling. The improved You Only Look Once-V4 (YOLO-V4) algorithm is combined with the channel pruning algorithm to detect the dropped ears of maize harvesters. K -means clustering algorithm is used to obtain a prior box matching the size of the dropped ears, which improves the Intersection Over Union (IOU). Compare the effect of different activation functions on the accuracy of the YOLO-V4 model, and use the Mish activation function as the activation function of this model. Improve the calculation of the regression positioning loss function, and use the CEIOU loss function to balance the accuracy of each category. Use improved Adam optimization function and multi-stage learning optimization technology to improve the accuracy of the YOLO-V4 model. The channel pruning algorithm is used to compress the model and distillation technology is used in the fine-tuning of the model. The final model size was only 10.77% before compression, and the test set mean Average Precision (mAP) was 93.14%. The detection speed was 112 fps, which can meet the need for real-time detection of maize harvester ears in the field. This study can provide technical reference for the detection of the ear loss rate of intelligent maize harvesters.

https://www.ijabe.org/index.php/ijabe/article/download/6660/pdf

Dynamic detection method for falling ears of maize harvester based on improved YOLO-V4

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Papers

Development and Testing of Automatic Row Alignment System for Corn Harvesters

Measuring the Thermal Properties of Pavement Materials

Detection of maize leaf diseases using improved MobileNet V3-small

Dynamic detection method for falling ears of maize harvester based on improved YOLO-V4